To assess the importance of various aspects of the model, we run experiments on GTA5 \(\rightarrow \)  Cityscapes task on ResNet-101 backbone, deactivating one or a few modules at a time while keeping the others activated. Besides, we test the combination performance between SIBAN and other DA methods [1]}, [2]}, in which the author suggests the channel-wise significance [2]} or the output [1]} should also be aligned between domains. We simply implement these two methods by adding two extra discriminators \(D\)  on significance tensors and segmentation maps, respectively. Table REF  shows the DA results under different settings. We observe that appending SA-layer can significantly improve the standard IB by \(1.5\%\) . Updating \(\beta _S/\beta _T\)  adaptively brings extra \(0.4\%\)  improvement as well. When employing two extra discriminators to the significance tensors and the segmentation maps, the target segmentation accuracy would be further improved by \(0.8\%\)  and \(2.3\%\) . The ablation study verifies the effectiveness of our SIB module as well as our “adaptive \(\beta \) ” strategy for DA task. Furthermore, SIBAN can be expediently combined with other DA methods to yield even better segmentation results on target images.
